Notice that we compute structure-aware scene representations \(g_i\)  via
random walks rather than stacking multiple GCN layers. We could in
theory stack two or three GCN layers and thus consider many more
representations (e.g., 100 or 1,000) for contextualizing \(g_i\) .
However, this would result to over-smoothed representations, that
converge to the same vector [1]}. Moreover, when
trying to contextualize such large neighborhoods in a graph, the
bottleneck phenomenon prevents the effective propagation of long-range
information [2]} which is our main goal. Finally,
in ”small world” networks with a few hops, neighborhoods could end
up containing the majority of the nodes in the graph
[3]}, which again hinders meaningful exploration
of long-range dependencies. Based on these limitations of GCNs, we
perform random walks, which allow us to consider a small number
of representations when contextualizing a scene, while also exploring
long-range dependencies.
